SPSS Base provides you with a wide range of statistics so you can get the most accurate response for specific data types. Add-on modules and other software provide you with even more analytical capabilities-and they easily plug into SPSS Base. This means you can add as much analytical capability to your system as you need and work seamlessly from one product to the next.
Linear Regression: Explore the relationships between predictors and what you want to predict; for example, predict sales using price and customer type.
Factor analysis: Identify underlying variables or factors that explain correlations within a set of observed variables. For example, use this procedure in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. Factor analysis includes a number of methods for factor extraction, rotation, and factor score computation.
TwoStep cluster analysis: Work with very large datasets using this scalable cluster analysis algorithm. TwoStep cluster analysis can handle continuous and categorical variables or attributes. This procedure enables you to group data so that records within a group are similar. For example, apply it to data that describe customer buying habits, gender, age, income, etc. Then, customize your marketing and product development strategy to each consumer group to increase sales and build brand loyalty.
Use TwoStep cluster analysis to more accurately identify clusters. This algorithm enables you to find clusters in large datasets and mixed datasets with continuous-
level variables (such as income) and categorical-level variables (such as type of policy).
K-means cluster analysis: Group data from larger datasets, such as customer mailing lists. This procedure assumes data fall into a known number of clusters. Given this number, the procedure will assign cases to clusters. You can select one of two methods to classify cases-either update cluster centers iteratively or classify only. You can then save cluster memberships, distance information, and final cluster centers. A market researcher, for example, might want to cluster cities into homogeneous groups using K-means cluster analysis to find comparable cities for test marketing.
Hierarchical cluster analysis: Take clusters from a single record and form groups until all clusters are merged. You can choose from more than 40 measures of similarity or dissimilarity, standardize data using several methods, and cluster cases or variables. You can also analyze raw variables or choose from a variety of standardizing transformations. Generate distance or similarity measures using the proximities procedure. Display statistics at each stage to help you select the best solution. This procedure is recommended for datasets that are smaller in number—for example, focus group lists. A market researcher could use hierarchical cluster analysis to identify types of television shows that attract similar audiences for each show type. The researcher could cluster TV shows into homogenous groups based on viewer characteristics to identify advertising segments.
Ordinal regression (PLUM): Make predictions with ordinal responses. For example, identify customer satisfaction level (very dissatisfied, somewhat dissatisfied, somewhat satisfied, or very satisfied) to understand customer loyalty. By choosing different link functions, you have the flexibility to fit ordinal logistic regression, ordinal probit models, and ordinal Cauchit models. You can also model both the location and scale of the underlying distribution. Ordinal regression gives you options to save predicted probabilities for all dependent variable categories back to your data.
Descriptive statistics
Bivariate statistics
Prediction for numerical outcomes and identifying groups
Add-on modules and stand-alone software from SPSS Inc. offer many more statistics and procedures, including the following:
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